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7 results for “Hakea”
Hakea vegetation dataset for training and evaluating segmentation network
<p>This dataset is the version used for both training and evaluation of the models discussed the (in press) publication in Methods in Ecology and Evolution, "Detecting plant species in the field with deep learning and drone technology".</p> <p>Also included in this upload, results_inference_images.tar includes the output predictions for each of the trained models on the unseen test set.</p>
Data from: The phylogeny and biogeography of Hakea (Proteaceae) reveals the role of biome shifts in a continental plant radiation
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Data from: Equilibrium and non-equilibrium phases in the radiation of Hakea and the drivers of diversity in Mediterranean-Type Ecosystems
Mediterranean-Type ecosystems (MTEs) contain exceptional plant diversity. Explanations for this diversity are usually classed as either "equilibrium", with elevated MTE diversity resulting from greater ecological carrying capacities, or "non-equilibrium", with MTEs having a greater accumulation of diversity over time. These models have typically been considered as mutually exclusive. Here we present a trait-based explanatory framework that incorporates both equilibrium and non-equilibrium dynamics. Using a large continental Australian plant radiation (Hakea) as a case study, we identify traits associated with niche partitioning in co-occurring species (α traits) and with environmental filtering (β traits), and reconstruct the mode and relative timing of diversification of these traits. Our results point to a radiation with an early, non-equilibrium phase marked by divergence of β traits as Hakea diversified exponentially and expanded from the southwest Australian MTE into biomes across the Australian continent. This was followed from 7Mya by an equilibrium phase, marked by diversification of α traits and a slowdown in lineage diversification as the MTE niches became saturated. These results suggest that processes consistent with both equilibrium and non-equilibrium models have been important during different stages of the radiation of Hakea, and together they provide a richer explanation of present-day diversity patterns.
FIGURE 1. Optimal tree inferred using the Neighbor-Joining method inferred from 550 in Paracoccus leucadendri Mazzeo & Franco in Mazzeo, Franco & Russo, 2009, a junior synonym of Paracoccus hakeae (Williams, 1985) comb. nov. (Coccomorpha: Pseudococcidae)
FIGURE 1. Optimal tree inferred using the Neighbor-Joining method inferred from 550 replicates. Shown next to the branches is the percentage of replicate trees in which the associated taxa clustered together in the bootstrap test. The tree is drawn to scale, with branch lengths in the same units as those of the genetic distances used to infer the tree. Genetic distances were computed using the p-distance method and are in the units of the number of base differences per site.
Data from: Equilibrium and non-equilibrium phases in the radiation of Hakea and the drivers of diversity in Mediterranean-Type Ecosystems
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HalALMT1 mediates malate efflux in the cortex of mature cluster rootlets of Hakea laurina, occurring naturally in phosphorus-poor soil
GEO Series GSE279190. Hakea laurina. 6 samples. Type: Expression profiling by high throughput sequencing.
Delayed leaf greening involves a major shift in expression of cytosolic and mitochondrial ribosomes to plastid ribosomes in the highly phosphorus-use-efficient Hakea prostrata (Proteaceae)
GEO Series GSE217069. Hakea prostrata. 26 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.